Heart Attack Risk Level Prediction System Using Navie Bayesian Classification

Abstract

Data Mining refers to using a variety of techniques to identify suggest of information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision support, predictions, forecasting and estimation. Classification is a major data mining task. Classification is a supervisedd leaning method to extract models describing important data classes or to predict future trends. In classification techniques, Naïve Bayesian Classifier is one of the simplest probabilistic classifiers. This paper presents the implementation of supervised learning algorithm, namely, Navie Bayes. The classification model will be derived from the training data by using the naïve Bayseian Classification algorithm. The model was then used for predicting the heart attack risk level of the patients. In addition, the prediction accuracy of the classifier was evaluated using holdout method and measured the accuracy by the confusion matrix. The users can check easily to know their risk levels of the heart attack by entering this system.